where \(\Vert \cdot \Vert _2\)  denotes the \(\ell _2\)  norm.
Graph sparsification heuristics based on \(k\) -nearest neighbors aim to reduce TSP graphs, enabling models to scale up to large instances where pairwise computation for all nodes is intractable [1]} or learn faster by reducing the search space [2]}.
Notably, problem-specific graph reduction techniques have proven effective for out-of-distribution generalization to larger graphs for other NP-hard problems such as MVC and SAT [3]}.
